Video Clustering Using SuperHistograms in Large Archives
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
A Clustering Technique for Video Copy Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Mining Similarities for Clustering Web Video Clips
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Video2Text: Learning to Annotate Video Content
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Ranking social bookmarks using topic models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Detecting events by clustering videos from large media databases
Proceedings of the 2nd ACM international workshop on Events in multimedia
Web Clustering Using Social Bookmarking Data with Dimension Reduction Regarding Similarity
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Clustering of Videos on a Video Sharing Site Using User-Contributed Comments
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Online Video Recommendation through Tag-Cloud Aggregation
IEEE MultiMedia
Knowing funny: genre perception and categorization in social video sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improved video categorization from text metadata and user comments
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Tag-based User Topic Discovery Using Twitter Lists
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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The spread of video sharing services has made available an enormous number of videos. Even when searching video sharing services, too many videos are returned to view. Viewers want to classify videos to easily grasp results, because videos may include varied topics or differing viewpoints. Video clustering is one solution to addressing this problem with many related approaches to research. However, existing approaches have problems: text information in metadata tends to be of low quality, visual information is difficult to analyze, and some information on user viewing behavior includes noise. This paper focuses on playlist information, which is a type of user viewing behavior. A playlist is useful because it is based on the viewers' knowledge or intuitiveness; beyond that, it is not noisy. We propose the playlist-based video clustering method (PVClustering), a novel framework that can form new clusters independent of text or visual similarities. The proposed method is computationally inexpensive and language-independent. By our method, users can grasp the outline of search result videos in a new light. Our experiments show good result clusters generated by PVClustering and prove that it can capture relativity or proximity among videos, which is not coded in text information. They also present the characteristics of PVClustering.